The use of two dimensional echocardiography imaging of the heart is an important noninvasive procedure in clinical cardiology. In particular, the endocardiac boundary of the two dimensional echocardiography image taken at the mitral valve level of the left ventricle (LV) of the heart, the short axis view, has been used for providing quantitative information about various cardiac functions such as the pressure-volume ratio, the ejection fraction, and cardiac wall motion. To quantify these functions, the contour of the LV needs to be first extracted and its cross-sectional area computed. Recently, the use of transesophageal echocardiography (TEE) has provided new apparatuses and methods for obtaining better quality images.
The problem of contour extraction based on two dimensional ultrasonic sector scans of any image, and in particular of the LV, is a difficult digital image processing problem because the images have very low spatial resolution, high levels of speckle noise, and, frequently the absence of an edge signal on some boundary conditions of the myocardial contour.
The prior art has identified four factors which affect the processing of images. First, the main component of image distortion is caused by the nonuniform reflection of the ultrasound beam from different parts of the body. The boundary angle relative to the ultrasound beam affects the reflective energy. For instance, the boundaries on the right and left side are often unclear because they are almost parallel to the ultrasound beam. Second, if an organ is in close proximity to a chamber or other object of interest, it may appear to be connected to that object. For example, in examining the heart, the papillary muscle sometimes appears as though it is connected to the wall, and sometimes as though it is not. Third, as mentioned above, speckle noise affects the processing of images. It is the main noise source in two dimensional echo images, and is caused by the nonfocused beam of the ultrasonic transducer and the non-homogeneous tissue structure of the body. Lastly, improper system calibrations can result in poor quality. This may generate a picture which is too bright or dark because of biasing error in the analogue circuit-mesh like lines which may appear on ultrasonic images are caused by improper sampling.
In general, the current contour detection schemes for contour extraction of the LV ultrasound image can be classified into three groups based on the processing strategies used. The first strategy is edge based. It consists of applying an edge operator to the ultrasound image and selecting a proper threshold value to determine the edge points. Several major stumbling blocks have to be successfully overcome when using this strategy. The first stumbling block is that the edge based strategy inherently involves the difficult problem of finding a proper threshold value. When a good threshold value is selected and all of the edges are extracted, there remains the additional problem of tracking all of the relevant edges which correspond to the LV boundary, and thereafter forming a closed contour from the boundary segments.
The second major strategy is region based or center based. The center of the region which corresponds to the LV cavity center is used to derive radial lines equally spaced in angles. A single boundary point is searched along each radial line. Filtering and interpolation methods are used to eliminate incorrect boundary points and to interpolate the closed contour. The center based method has the advantage of reducing the boundary searching problem from two dimensions to only one dimension.
Three problems can readily be identified with the region based or center based strategy. The first problem is finding the center of the LV automatically. This problem requires adequate region segmentation procedures which are not simple due to the low contrast and high noise content of the signal. Simple thresholding schemes do not yield satisfactory results. Therefore, the center is normally located manually by a human operator with a pointing device.
The second problem is that the region based or center based strategy cannot find the correct boundary points when the contour is not all convexed or protrusions are formed on the boundary. The concavity of the contour will cause a problem because a single point per radial line is normally assumed. This approach cannot find a boundary point correctly if the contour is nearly parallel to the radial line wherein multiple points along a radial line may belong to the boundary contour. In the short axis LV images, there are protrusions on the LV boundary due to the trabeculation and the papillary muscles. The two major protrusions normally correspond to the papillary muscles of the LV. The concave curve on the contour, formed by the papillary muscle, is referred to herein as a cave. The presence of such a cave means that the radial line may intersect with the boundary at two additional points.
A third problem with the region based or center based strategy is that radial line search scheme is very noise sensitive. Because of this, it is difficult to identify and delete erroneous boundary points caused by the "noisy blobs" within the region.
The third strategy is sequential frame based. Boundary contours are determined by providing a reference contour outline first by human operator. This strategy offers improved reliability by using statistical information of sequential frames. However, the methods of this type still rely on manual operation on either one or several frames.
Filtering and noise deletion is important to the proper operation of this system. However, improvements in the quality of the ultrasonic image and signal from the ultrasonic transducer, or enhancements within the ultrasonic system, will reduce the need for filtration or smoothing.
Thus, the prior art indicates that the determination and extraction of relevant image points such as region points, the region center, or the boundary points has proved to be very difficult. Semi-automatic methods rather than fully automated methods have been used because the two dimensional echocardiography LV images lack boundary signals in certain portions of the image, and have high noise content. There is a need in the art for a system which can automatically and reliably determine and extract features of an imaged object, and in particular the boundary contour of the left ventricle of the heart, on a real time basis, even in the presence of poor imagery. The method should also provide information which may be used in quantitative analysis of the boundary.